Simple unconventional geometric scenario of one-way quantum computation with superconducting qubits inside a cavity

We propose a simple unconventional geometric scenario to achieve a kind of nontrivial multi-qubit operations with superconducting charge qubits placed in a microwave cavity. The proposed quantum operations are insensitive not only to the thermal state of cavity mode but also to certain random operation errors, and thus may lead to high-fidelity quantum information processing. Executing the designated quantum operations, a class of highly entangled cluster states may be generated efficiently in the present scalable solid-state system, enabling one to achieve one-way quantum computation.Comment: Accepted version with minor amendments. To appear in Phys. Rev.

Nitrate sources and dynamics in a salinized river and estuary : a δ15N-NO₃⁻ and δ18O-NO₃⁻ isotope approach

To trace NO3- sources and assess NO3- dynamics in salinized rivers and estuaries, three rivers (Haihe River: HH River, Chaobaixin River: CB River and Jiyun River: JY River) and two estuaries (HH Estuary and CJ Estuary) along the Bohai Bay (China) have been selected to determine dissolved inorganic nitrogen (DIN: NH4+, NO2- and NO3-. Upstream of the HH River, NO3- was removed 30.9 +/- 22.1% by denitrification, resulting from effects of the floodgate: limiting water exchange with downstream and prolonging water residence time to remove NO3-. Downstream of the HH River NO3- was removed 2.5 +/- 13.3% by NO3- turnover processes. Conversely, NO3- was increased 36.6 +/- 25.2% by external N source addition in the CB River and 34.6 +/- 35.1% by instream nitrification in the JY River. The HH and CY Estuaries behaved mostly conservatively excluding the sewage input in the CJ Estuary. Hydrodynamics in estuaries has been changed by the ongoing reclamation projects, aggravating the loss of the attenuation function of NO3- in the estuary

The influences of the galactic cosmic ray on the atmospheric ozone

The relationship between the yearly variations of cosmic ray intensity and ozone in the atmosphere, and the ozone disturbance initiated by the Forbush decrease of 1965-1976 is analyzed. The data on cosmic ray intensity were selected from the records of the super neutron monitor at Deep River station and the ionization chamber at Beijing station. Ozone data were taken from Resolute (Canada), Bismark (N. Dakota, USA), Kagoshima (Japan), and Kodaikanal (India). The statistical results show that ozone is prominently modulated and disturbed by the 11 year variation and the Forbush decrease in the galactic cosmic ray

Universal holonomic quantum gates in decoherence-free subspace on superconducting circuits

To implement a set of universal quantum logic gates based on non-Abelian geometric phases, it is a conventional wisdom that quantum systems beyond two levels are required, which is extremely difficult to fulfil for superconducting qubits, appearing to be a main reason why only single qubit gates was implemented in a recent experiment [A. A. Abdumalikov Jr \emph{et al}., Nature 496, 482 (2013)]. Here we propose to realize non-adiabatic holonomic quantum computation in decoherence-free subspace on circuit QED, where one can use only the two levels in transmon qubits, a usual interaction, and a minimal resource for the decoherence-free subspace encoding. In particular, our scheme not only overcomes the difficulties encountered in previous studies, but also can still achieve considerably large effective coupling strength, such that high fidelity quantum gates can be achieved. Therefore, the present scheme makes it very promising way to realize robust holonomic quantum computation with superconducting circuits.Comment: V4: published version; V1: submitted on April

Quantum computation in decoherence-free subspace with superconducting devices

We propose a scheme to implement quantum computation in decoherence-free subspace with superconducting devices inside a cavity by unconventional geometric manipulation. Universal single-qubit gates in encoded qubit can be achieved with cavity assisted interaction. A measurement-based two-qubit Controlled-Not gate is produced with parity measurements assisted by an auxiliary superconducting device and followed by prescribed single-qubit gates. The measurement of currents on two parallel devices can realize a projective measurement, which is equivalent to the parity measurement on the involved devices.Comment: v2: thoroughly rewritten version with title and motivation changed; v3: published version with detail dirivation

The matched subspace detector with interaction effects

This paper aims to propose a new hyperspectral target-detection method termed the matched subspace detector with interaction effects (MSDinter). The MSDinter introduces “interaction effects” terms into the popular matched subspace detector (MSD), from regression analysis in multivariate statistics and the bilinear mixing model in hyperspectral unmixing. In this way, the interaction between the target and the surrounding background, which should have but not yet been considered by the MSD, is modelled and estimated, such that superior performance of target detection can be achieved. Besides deriving the MSDinter methodologically, we also demonstrate its superiority empirically using two hyperspectral imaging datasets

Multi-Estimator Full Left Ventricle Quantification through Ensemble Learning

Cardiovascular disease accounts for 1 in every 4 deaths in United States. Accurate estimation of structural and functional cardiac parameters is crucial for both diagnosis and disease management. In this work, we develop an ensemble learning framework for more accurate and robust left ventricle (LV) quantification. The framework combines two 1st-level modules: direct estimation module and a segmentation module. The direct estimation module utilizes Convolutional Neural Network (CNN) to achieve end-to-end quantification. The CNN is trained by taking 2D cardiac images as input and cardiac parameters as output. The segmentation module utilizes a U-Net architecture for obtaining pixel-wise prediction of the epicardium and endocardium of LV from the background. The binary U-Net output is then analyzed by a separate CNN for estimating the cardiac parameters. We then employ linear regression between the 1st-level predictor and ground truth to learn a 2nd-level predictor that ensembles the results from 1st-level modules for the final estimation. Preliminary results by testing the proposed framework on the LVQuan18 dataset show superior performance of the ensemble learning model over the two base modules.Comment: Jiasha Liu, Xiang Li and Hui Ren contribute equally to this wor